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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

A)

B)

C)

D)

2. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Locate the executor logs on the Spark master node, typically under the /tmp directory.
B) Fetch the logs by running a Spark job with the spark-sql CLI tool.
C) Use the command spark-submit with the -verbose flag to print the logs to the console.
D) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.

3. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.orderBy("timestamp").limit(4)
B) streaming_df. select (countDistinct ("Name") )
C) streaming_df.groupby("Id") .count ()
D) streaming_df.filter (col("count") < 30).show()

4. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) Transformations are executed immediately to build the lineage graph.
B) The Spark engine optimizes the execution plan during the transformations, causing delays.
C) Transformations are evaluated lazily.
D) Only actions trigger the execution of the transformation pipeline.
E) The Spark engine requires manual intervention to start executing transformations.

5. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 80
B) 40
C) 160
D) 64

Solutions: